자율주행 칩 시장 기회, 성장 촉진요인, 산업 동향 분석 및 예측(2025-2034년)

The Global Autonomous Driving Chips Market was valued at USD 24.22 billion in 2024 and is estimated to grow at a CAGR of 23% to reach USD 191.07 billion by 2034.

Autonomous driving chips are purpose-built processors that enable intelligent vehicle functionality, executing critical functions such as real-time path planning, environmental perception, sensor data fusion, and autonomous decision-making. As automakers steadily move toward higher levels of automation, from basic driver assistance to full autonomy, the need for chips that can deliver ultra-low latency and high reliability has intensified. The widespread adoption of advanced driver-assistance systems (ADAS), along with the industry's pivot to electric vehicles, is amplifying the demand for high-performance chips that offer scalability, energy efficiency, and precision computing. Regulatory pressure to enhance road safety is also encouraging automotive OEMs to integrate smarter electronic architectures. Automakers are transitioning away from traditional processor-based platforms in favor of chipsets that provide better power management and performance optimization. This shift supports improved design flexibility and allows cost-effective deployment of autonomous technologies across vehicle categories, driving the overall momentum of the global market.

In 2024, the application-specific integrated circuits (ASIC) segment held a 36% share and is forecast to grow at a CAGR of 25% through 2034. These chips are highly optimized to handle defined tasks, such as sensor fusion, machine vision, and neural network acceleration. Their dedicated architecture results in greater computational efficiency, reduced latency, and thermal stability, key benefits in automotive environments where compact size, power optimization, and safety are critical. ASICs are designed with specific workloads in mind, making them especially valuable in autonomous driving, where precision and reliability are non-negotiable.

The Level 1 (driver assistance) segment held a 45% share in 2024 and is estimated to grow at a CAGR of 18.8% from 2025 to 2034. While the market is trending toward higher levels of automation, Level 1 remains dominant due to its affordability, ease of integration, and reliance on mature technologies. Level 2 capabilities are growing in prominence, supported by consumer interest in partial automation and growing regulatory focus on safety enhancement. However, Level 1 solutions continue to be favored in mass-market vehicles due to their cost-effectiveness and lower complexity.

North America Autonomous Driving Chips Market held a 35% share and generated USD 8.54 billion in 2024. The region, particularly the United States, is a dominant force due to a blend of advanced R&D capabilities, favorable policy direction, mature semiconductor manufacturing infrastructure, and widespread deployment of real-world testing programs. This environment has created strong momentum for innovation and commercialization of autonomous chip technologies, driving rapid adoption in passenger cars, electric vehicles, and connected mobility platforms.

Key players in the Global Autonomous Driving Chips Market include STMicroelectronics, Texas Instruments, Intel (Mobileye), Qualcomm, Infineon Technologies, NVIDIA, Renesas Electronics, Analog Devices, and NXP Semiconductors. To solidify their competitive edge in the autonomous driving chips market, companies are investing in next-generation chip architectures purpose-built for edge AI processing, low-latency control, and real-time sensor interpretation. Strategic alliances with automotive OEMs, Tier-1 suppliers, and AI software vendors enable tighter integration of hardware and autonomous driving stacks. R&D spending is directed toward improving chip scalability, reducing energy consumption, and enabling higher-level automation in a smaller silicon footprint.

Table of Contents

Chapter 1 Methodology

• 1.1 Market scope and definition
• 1.2 Research design
  • 1.2.1 Research approach
  • 1.2.2 Data collection methods
• 1.3 Data mining sources
  • 1.3.1 Global
  • 1.3.2 Regional/Country
• 1.4 Base estimates and calculations
  • 1.4.1 Base year calculation
  • 1.4.2 Key trends for market estimation
• 1.5 Primary research and validation
  • 1.5.1 Primary sources
• 1.6 Forecast
• 1.7 Research assumptions and limitations

Chapter 2 Executive Summary

• 2.1 Industry 360° synopsis, 2021 - 2034
• 2.2 Key market trends
  • 2.2.1 Regional
  • 2.2.2 Chip
  • 2.2.3 Autonomy level
  • 2.2.4 Function
  • 2.2.5 Vehicle
• 2.3 TAM analysis, 2025-2034
• 2.4 CXO perspectives: Strategic imperatives
  • 2.4.1 Executive decision points
  • 2.4.2 Critical success factors
• 2.5 Future-outlook and strategic recommendations

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
  • 3.1.1 Supplier landscape
  • 3.1.2 Profit margin analysis
  • 3.1.3 Cost structure
  • 3.1.4 Value addition at each stage
  • 3.1.5 Factors affecting the value chain
  • 3.1.6 Disruptions
• 3.2 Industry impact forces
  • 3.2.1 Growth drivers
    • 3.2.1.1 Rising adoption of sae level 2+ and level 3 vehicles
    • 3.2.1.2 Rapid advancements in AI and edge computing technologies
    • 3.2.1.3 Increased investments by automotive oems and tier-1 suppliers
    • 3.2.1.4 Government support and AV-friendly regulations
    • 3.2.1.5 Growing safety concerns and accident reduction initiatives
    • 3.2.1.6 Expansion of electric and software-defined vehicle architectures
  • 3.2.2 Industry pitfalls and challenges
    • 3.2.2.1 High development and manufacturing costs of advanced chips
    • 3.2.2.2 Complexity of functional safety certification (ASIL-D, ISO 26262)
  • 3.2.3 Market opportunities
    • 3.2.3.1 Emergence of level 4/5 autonomous commercial fleets
    • 3.2.3.2 Growing demand in Asia Pacific and Middle East regions
    • 3.2.3.3 Rise of chiplet and modular architectures
    • 3.2.3.4 Integration with 5G and V2X communication technologies
• 3.3 Growth potential analysis
• 3.4 Patent analysis
• 3.5 Porter’s analysis
• 3.6 PESTEL analysis
• 3.7 Cost breakdown analysis
• 3.8 Technology and innovation landscape
  • 3.8.1 Current technological trends
    • 3.8.1.1 Computer vision algorithm evolution
    • 3.8.1.2 Sensor fusion technology trends
    • 3.8.1.3 Edge computing advancement
    • 3.8.1.4 Real-time processing innovations
  • 3.8.2 Emerging technologies
• 3.9 Regulatory landscape
  • 3.9.1 Global regulatory framework overview
  • 3.9.2 NHTSA autonomous vehicle guidelines
  • 3.9.3 European type approval requirements
  • 3.9.4 China's national standards (GB/T)
  • 3.9.5 Emerging regulatory trends
• 3.10 Price trends
  • 3.10.1 By region
  • 3.10.2 By chip
• 3.11 Production statistics
  • 3.11.1 Production hubs
  • 3.11.2 Consumption hubs
  • 3.11.3 Export and import
• 3.12 Sustainability & ESG impact analysis
  • 3.12.1 Green manufacturing practices
  • 3.12.2 Energy efficiency optimization
  • 3.12.3 Waste reduction in fab operations
  • 3.12.4 Sustainable material usage
• 3.13 Investment & funding trends analysis
• 3.14 Quality and reliability standards
  • 3.14.1 ISO 26262 functional safety (ASIL-D)
  • 3.14.2 AEC-Q100 automotive qualification
  • 3.14.3 Cybersecurity standards (ISO 21434)
  • 3.14.4 AI safety and validation requirements
• 3.15 Digital transformation impact
  • 3.15.1 AI-driven design automation
  • 3.15.2 Digital twin implementation
  • 3.15.3 Cloud-based development
  • 3.15.4 DevOps in chip development
• 3.16 Supply chain resilience assessment
  • 3.16.1 Critical material dependencies
  • 3.16.2 Geographic concentration risks
  • 3.16.3 Single point of failure analysis
  • 3.16.4 Supply chain diversification
  • 3.16.5 Alternative sourcing strategies
  • 3.16.6 Supply chain transparency

Chapter 4 Competitive Landscape, 2024

• 4.1 Introduction
• 4.2 Company market share analysis
  • 4.2.1 North America
  • 4.2.2 Europe
  • 4.2.3 Asia Pacific
  • 4.2.4 Latin America
  • 4.2.5 Middle East & Africa
• 4.3 Competitive analysis of major market players
• 4.4 Competitive positioning matrix
• 4.5 Strategic outlook matrix
• 4.6 Key developments
  • 4.6.1 Mergers & acquisitions
  • 4.6.2 Partnerships & collaborations
  • 4.6.3 New product launches
  • 4.6.4 Expansion plans and funding

Chapter 5 Market Estimates & Forecast, By Chip, 2021 - 2034 (USD Bn, Million Units)

• 5.1 Key trends
• 5.2 Microcontrollers (MCUs)
• 5.3 GPU
• 5.4 FPGA
• 5.5 ASIC
• 5.6 Others

Chapter 6 Market Estimates & Forecast, By Autonomy Level, 2021 - 2034 (USD Bn, Million Units)

• 6.1 Key trends
• 6.2 Level 1 (driver assistance)
• 6.3 Level 2 (partial automation)
• 6.4 Level 3 (conditional automation)
• 6.5 Level 4 (high automation)
• 6.6 Level 5 (full automation)

Chapter 7 Market Estimates & Forecast, By Function, 2021 - 2034 (USD Bn, Million Units)

• 7.1 Key trends
• 7.2 Perception chips
• 7.3 Decision-making chips
• 7.4 Control chips

Chapter 8 Market Estimates & Forecast, By Vehicle, 2021 - 2034 (USD Bn, Million Units)

• 8.1 Key trends
• 8.2 Passenger
• 8.3 Commercial

Chapter 9 Market Estimates & Forecast, By Region, 2021 - 2034 (USD Bn, Million Units)

• 9.1 Key trends
• 9.2 North America
  • 9.2.1 US
  • 9.2.2 Canada
• 9.3 Europe
  • 9.3.1 Germany
  • 9.3.2 UK
  • 9.3.3 France
  • 9.3.4 Italy
  • 9.3.5 Spain
  • 9.3.6 Nordics
  • 9.3.7 Russia
• 9.4 Asia Pacific
  • 9.4.1 China
  • 9.4.2 India
  • 9.4.3 Japan
  • 9.4.4 Australia
  • 9.4.5 Indonesia
  • 9.4.6 Philippines
  • 9.4.7 Thailand
  • 9.4.8 South Korea
  • 9.4.9 Singapore
• 9.5 Latin America
  • 9.5.1 Brazil
  • 9.5.2 Mexico
  • 9.5.3 Argentina
• 9.6 Middle East and Africa
  • 9.6.1 Saudi Arabia
  • 9.6.2 South Africa
  • 9.6.3 UAE

Chapter 10 Company Profiles

• 10.1 Global Players
  • 10.1.1 Analog Devices
  • 10.1.2 Infineon Technologies
  • 10.1.3 Intel (Mobileye)
  • 10.1.4 NVIDIA
  • 10.1.5 NXP Semiconductors
  • 10.1.6 ON Semiconductor
  • 10.1.7 Qualcomm
  • 10.1.8 Renesas Electronics
  • 10.1.9 STMicroelectronics
  • 10.1.10 Texas Instruments
• 10.2 Regional Players
  • 10.2.1 Ambarella
  • 10.2.2 Black Sesame Technologies
  • 10.2.3 Cambricon Technologies
  • 10.2.4 Esperanto Technologies
  • 10.2.5 Hailo Technologies
  • 10.2.6 Horizon Robotics
  • 10.2.7 Kalray
  • 10.2.8 Kneron
• 10.3 Emerging Players / Disruptors
  • 10.3.1 AImotive
  • 10.3.2 Blaize
  • 10.3.3 BrainChip
  • 10.3.4 Eta Compute
  • 10.3.5 Flex Logix
  • 10.3.6 GreenWaves Technologies
  • 10.3.7 Recogni
  • 10.3.8 Syntiant